Сравнение методов

	Генерация с дополненной выборкой (Retrieval-Augmented Generation, RAG)	Многоголовочное самовнимание
Область	Интеллектуальный анализ текста	Глубокое обучение
Семейство	Process / pipeline	Machine learning
Год появления	2020	2017
Автор метода	Lewis, Patrick et al. (Meta AI / Facebook AI Research)	Vaswani, A. et al.
Тип	Hybrid retrieval + generation pipeline	Attention mechanism (Transformer core)
Основополагающий источник	Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Другие названия	RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG)	Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention
Связанные	7	5
Сводка	Retrieval-Augmented Generation (RAG) is a natural-language-processing pipeline introduced by Lewis et al. in 2020 that strengthens a large language model (LLM) with evidence fetched at inference time from an external knowledge base. Instead of relying solely on what a model memorised during training, RAG first retrieves the most relevant passages from a document index and then hands those passages to the LLM as context, grounding the generated answer in verifiable, up-to-date information. The approach reduces hallucination and allows domain-specific or time-sensitive knowledge to be injected without retraining the model.	Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5.